Report of the Advisory Panel on Streamlining and Codifying Acquisition Regulations

Volume 3 of 3 | January 2019

Acquisition Data Volume 3

Recommendation 88: Use existing defense business system open-data 

requirements to improve strategic decision making on acquisition and 

workforce issues.

Problem

In recent decades, DoD and the rest of the U.S. government have spent tens of billions of dollars on 

software development, data architectures, and business processes to enable collection of vast amounts 

of acquisition and financial data. There has not been a similarly scoped effort to build up DoD’s data 

analytics capabilities or use those capabilities for strategic decision making. DoD currently has access to 

a vast amount of acquisition and financial data, but in too many cases lacks the ability to do anything 

useful with the data.

Background

The U.S. government manages many systems for collecting and reporting information related to 

defense acquisition. Defense business systems contain enterprise data on regulations and laws, 

acquisition requirements, budgeting and appropriations, program management, contract solicitations, 

contract awards, contract vendors, and other parts of the broader acquisition process.1

When these datasets are viewed at an aggregate level, analysts can perceive patterns and policy makers 

can form conclusions that might not be possible by looking at individual data points at the working 

level. Many stakeholders, however, have noted that DoD has limited abilities to engage in this type of 

aggregate information analysis. Inadequate data science training and recruitment is part of the reason 

for this problem.2 Information siloes and the resulting unavailability of data across DoD are also key 

factors.3

This data silo phenomenon can lead to situations in which one office collects data that is highly 

applicable to the work of another office but unavailable to them. In some cases, the problem is that key 

personnel lack access to the data in question. In other cases, the problem is simply that personnel are 

unaware the data exist.

What is Data Analytics?

Terms such as data science, data analysis, and data analytics are frequently used interchangeably in 

informal conversation. For the purposes of this paper, data science is considered a more narrowly 

 

1 Defense business systems (DBSs) are defined in statute under 10 U.S.C. § 2222 and in DoD policy under DoDI 5000.75. For detailed 

recommendations on process improvements to DBS acquisition, see Section 809 Panel, Report of the Advisory Panel on Streamlining and 

Codifying Acquisition Regulations: Volume 1 of 3, 103-148 (2018). 2 See, for example, the 2018 National Defense Strategy, which states that DoD will “emphasize new skills and complement our current 

workforce with information experts [and] data scientists” who are able “to use information, not simply manage it.” From Department of 

Defense, Summary of the 2018 National Defense Strategy, 8, accessed May 21, 2018, 

https://www.defense.gov/Portals/1/Documents/pubs/2018-National-Defense-Strategy-Summary.pdf. 3 See, for example, a statement from the DoD CIO in 2017: “The current approach of unique systems with stove-piped data sources is a 

high risk, high cost approach. It ensures long developmental lead times and fragile solutions that cannot be transferred to other 

operations.” “DoD’s Mission Partner Environment – Information System (MPE-IS),” DoD Chief Information Officer, accessed May 21, 

2018, http://dodcio.defense.gov/In-the-News/MPE.
Report of the Advisory Panel on Streamlining and Codifying Acquisition Regulations

Volume 3 of 3 | January 2019

Acquisition Data Volume 3

defined and rigorous field, which combines formal methods from academic disciplines such as 

statistics, computer science, and mathematics.

Data analysis and data analytics are used interchangeably here to describe a broader skill set that does 

not necessarily involve formal academic methods. Data analysts may work in an academic setting, but 

in DoD they are more likely to work in a policy setting, providing information as needed to support 

decision makers.

Data architecture is another term frequently used to refer to the way information is organized. The first 

section of this paper focuses on the role of data analytics in DoD’s strategic decision making, and does 

not address the issue of data architecture.

Open Data Requirement Within DoD

Both DoD leadership and Congress have in recent years encouraged DoD to improve its ability to 

analyze and use its own acquisition data.4 In Section 911 of the FY 2018 NDAA, Congress mandated an 

open data policy within DoD, requiring that “except as otherwise provided by law or regulation,” DoD 

business enterprise data “shall be made readily available” to military departments, combatant 

commands, and “all other offices, agencies, activities, and commands of the Department of Defense.”5

Section 912 of the FY 2018 NDAA codified the requirements for departmentwide transparency in 

enterprise-level datasets.6 The section also specified the DoD CMO as the owner of “primary decisionmaking authority with respect to the development of common enterprise data.”7 The section required 

the CMO to set up “a data analytics capability” in support of “enhanced oversight and management” 

and launch pilot programs “to develop data integration strategies... to address high-priority 

management challenges.”8

As of mid-2018, the DoD CMO’s office had begun work to implement the FY 2018 provisions, 

establishing information sharing systems such as the CMO Connect Portal website. The site is intended 

as a “resource and collaboration tool for DoD service members, civilian employees and contractors.”9

Data as a Tool for Strategic Decision Making

Stakeholders within Congress and DoD have long pushed for greater use of existing data in strategic 

decision making. The Congressional Research Service has noted that DoD agencies “lag behind the 

 

4 The Joint Staff, for example, publishes a guide to best practices on knowledge and information management, which advocates for “flat, 

transparent networks to share and retain information” instead of an “exclusive, stove-piped approach to information sharing and 

decision-making.” See J7 Deputy Director for Joint Training, Insights and Best Practices Focus Paper on Knowledge and Information 

Management, May 2018, 13, accessed May 21, 2018, 

http://www.jcs.mil/Portals/36/Documents/Doctrine/fp/knowledge_and_info_fp.pdf. 5 Section 911 of FY 2018 NDAA, Pub. L. No. 115-91 (2017). 6 Section 911 of FY 2018 NDAA, Pub. L. No. 115-91 codified the transparency requirements under 10 U.S.C. § 2222 (with conforming 

amendments to other sections of U.S. Code under Title 10).

7 Section 911 of FY 2018 NDAA, Pub. L. No. 115-91 (2017). 8 Ibid. 9 “Chief Management Officer,” DoD, accessed July 27, 2018, https://cmo.defense.gov. As of July 2018, little departmentwide acquisition 

data had been made accessible via the site.
Report of the Advisory Panel on Streamlining and Codifying Acquisition Regulations

Volume 3 of 3 | January 2019

Acquisition Data Volume 3

private sector in effectively incorporating data analyses into decisionmaking.”10 The Government 

Accountability Office (GAO) has reported that in some cases, despite being required by statute, DoD 

management data analysis “did not assess the reliability of the data used, define key terms, clearly state 

criteria used for analysis, or make recommendations.”11 The GAO also publishes a best practices guide 

to support improved data-driven decision making in federal agencies.12

Section 913 of the FY 2018 NDAA mandated that DoD “establish a set of activities that use data 

analysis, measurement, and other evaluation-related methods to improve the acquisition outcomes.”13

Congress gave DoD broad discretion over what exactly these activities should entail.

The DoD Chief Information Officer (CIO) serves as a key maintainer of the IT systems in which much 

of DoD’s business data are housed. Once such systems have been built, it is critical to have an entity 

dedicated to maintenance, cybersecurity, and related software issues. The CIO serves in this role, and 

can also provide expertise on development of needed application program interfaces to provide 

departmentwide access to DoD management data.

Hiring and Financial Assistance Authorities

The difficulty of recruiting and training qualified talent is a key impediment to improving data 

analytics in DoD. The CMO’s 2018 report on DoD business operations specifically cited the need for 

“the skills and knowledge to analyze results and suggest improvements as needed” as a key focus area 

for using data to drive decisions.14

To enable DoD to bring in skilled technical ability, Congress has enacted many special hiring 

authorities targeting personnel with skills in science, engineering, management, and related disciplines. 

Many of these authorities are pilot programs; others are permanent.15 Several of the authorities may be 

applicable to recruitment and training of data science professionals.

Automation and Artificial Intelligence

Business process automation can greatly reduce the time and labor required to conduct data analysis. 

Increasing use of automated analytical tools, sometimes referred to under the umbrella label of artificial 

intelligence (AI), promises to increase the efficiency of DoD’s business data analysis in coming years.

Discussion

DoD has spent tens of billions of dollars in recent decades building up its data collection capabilities in 

the form of IT infrastructure, custom-built software, and policy documents to establish business 

 

10 Moshe Schwartz, Using Data to Improve Defense Acquisitions: Background, Analysis, and Questions for Congress, Congressional 

Research Service, January 5, 2016, accessed November 7, 2018, https://fas.org/sgp/crs/natsec/R44329.pdf. 11 GAO, Defense Management: DOD Needs to Address Inefficiencies and Implement Reform across Its Defense Agencies and DOD Field 

Activities, GAO-18-592, September 2018,10, accessed September 13, 2018, https://www.gao.gov/assets/700/694333.pdf. 12 “Data-Driven Decision Making,” GAO, accessed July 27, 2018, https://www.gao.gov/key_issues/data-driven_decision_making. 13 Section 913 of FY 2018 NDAA, Pub. L. No. 115-91 (2017). 14 DoD Chief Management Officer, FY 2018 – FY 2022 National Defense Business Operations Plan, 36, accessed November 7, 2018, 

https://cmo.defense.gov/Portals/47/Documents/Publications/NBDOP/TAB%20B%20FY18-22%20NDBOP%20Appendices.pdf?ver=2018-

05-25-131454-683. 15 See Section 809 Panel, Report of the Advisory Panel on Streamlining and Codifying Acquisition Regulations: Volume 2 of 3, 61-100 

(2018) for analysis and lists of special hiring authorities available to DoD. Also see Appendix C of this Volume 3 Report for hiring and 

financial assistance authorities that may apply to data scientists.
Report of the Advisory Panel on Streamlining and Codifying Acquisition Regulations

Volume 3 of 3 | January 2019

Acquisition Data Volume 3

processes. Thanks to massive investment of time and money, DoD now has access to a wealth of

acquisition and financial data. Some policy and decision-making offices, however, lack the ability to 

properly use the data that DoD collects.

In the FY 2018 NDAA, Congress instructed DoD to embrace an open-data philosophy, providing 

defense business system data to analysts throughout the organization. This effort is a positive first step 

toward building a more effective data analytics culture within DoD. It will fall to the CMO and related 

offices to ensure open availability of data across DoD, and it will fall to individual offices to fully use

those datasets in their day-to-day decision making.

In addition to congressional and DoD efforts to improve data analytics, the Office of Management and 

Budget (OMB) and related executive agencies promote a Federal Data Strategy.16 The strategy is 

intended to provide a broad, long-term framework for improving agencies’ data stewardship and 

leveraging data to create value. A late-2018 draft of the strategy includes 47 goals (practices), many of 

which overlap with the goals of data management provisions in the FY 2018 NDAA.

Table 9-1. Selected Federal Data Strategy Draft Practices17

Draft Practice OMB Description

2: Inventory Data Assets Maintain an inventory of data assets with sufficient completeness, quality, and 

metadata to facilitate planning, discovery, access, and use.

3: Identify High-Value and 

Authoritative Data Assets

Assign value and cost to data assets based on usefulness, applicable law, regulation, 

policy, and operational guidance to appropriately prioritize and document 

stewardship and resource decisions.

4: Align Resources to Value and 

Authority

Periodically review alignment of resources to the value and authority of datasets to 

promote consistency and fairness.

5: Manage High-Value and 

Authoritative Data Assets

Periodically review high-value and authoritative data assets to identify and document 

opportunities to improve data management systems and procedures and ensure 

quality and integrity.

13: Diversify Data Access 

Methods

Invest in the creation and usability of multiple tiers of access to federal data by 

committing federal resources to making data as open and accessible as possible while 

protecting confidentiality.

14: Innovate to Enable Safe Use Explore and periodically review methods and technologies that enable tiered access 

to safeguard data and promote accessibility to relevant stakeholders.

19: Prepare to Share Provide encouragement and incentives for agencies to develop a culture in which 

they are predisposed to share data within and across federal agencies, as well as with 

external partners, with proper protections and where relevant and appropriate.

 

16 The Office of Management and Budget, Office of Science and Technology Policy, Department of Commerce, Small Business 

Administration, and General Services Administration were all involved in the development of the Federal Data Strategy as of late 2018. 

See Federal Data Strategy introduction at: https://strategy.data.gov. 17 From Office of Management and Budget, “Federal Data Strategy: The Draft Practices,” https://strategy.data.gov/practices.
Report of the Advisory Panel on Streamlining and Codifying Acquisition Regulations

Volume 3 of 3 | January 2019

Acquisition Data Volume 3

Draft Practice OMB Description

20: Share Data Across Agencies Facilitate data sharing across federal agencies to efficiently generate more 

comprehensive data for improved decision making.

34: Promote a Culture that 

Values Data as an Asset

Conduct routine assessments of current organizational practices to identify 

opportunities to improve the agency's ability to acquire, use, and disseminate data 

for program, statistical, and mission-support purposes to improve data use and value.

36: Incorporate Data into 

Decision-Making

As part of budget, operational, policy, and management processes, identify 

opportunities to effectively and routinely use data for decision making and to create a 

bridge between evaluation, performance, and other activities within agencies.

37: Communicate Insights from 

Data

Adopt a range of innovative communication tools and techniques to effectively 

transmit insights from data to a broad set of consumers, both internal and external to 

the agency.

38: Connect Federal Spending to 

Outcomes

Analyze spending data to align resources with strategic priorities and desired 

outcomes to enable the public to understand the results of federal investments and 

to support informed decision making regarding future investments.

39: Focus on End Uses of Data Design new data collections with the end uses in mind to ensure that the data 

collected will be of appropriately high quality and meet internal and external 

stakeholder expectations and needs.

40: Assess the Needs of 

Stakeholders

Routinely engage both internal and external stakeholders throughout the data 

lifecycle to assess the needs of data consumers.

Conclusions

One of the impediments to DoD data analysis is the fact that many information systems are siloed off 

from senior decision makers. Even within DoD acquisition and financial circles, many of these systems 

are not widely understood and lack enough proficient users to provide useful analysis at the policy 

level. Congress has helped to correct this problem by enacting the open data requirements of the 

FY 2018 NDAA, but implementing it will likely be a long and technically challenging process.

The CMO should take the lead in this effort, in part by reminding DoD of the requirement and 

providing portals through which personnel can access defense business data collected by entities 

elsewhere in DoD.

The CMO should also take steps to ensure that Defense Agency leaders and data analysts are aware of 

what data exist outside of their offices. Senior leaders and analysts should be encouraged to think 

creatively about the ways they might use data from outside their areas of functional expertise.

The CMO should also encourage Defense Agencies to recruit more data science professionals and train 

current personnel in data analytics. Existing special hiring and education financial assistance 

authorities may, in some cases, be used for these purposes.

An Executive-Legislative Grand Bargain?

If DoD offices fully opened up their business system data to scrutiny by the rest of DoD and Congress, 

it could allow for side benefits in the form of a grand bargain on acquisition and financial data as used 
Report of the Advisory Panel on Streamlining and Codifying Acquisition Regulations

Volume 3 of 3 | January 2019

Acquisition Data Volume 3

by the oversight community. Open access to data systems would provide much deeper insight into 

individual transactions. With such insight, congressional committees might show greater confidence 

that effective oversight could be conducted reactively rather than proactively, on a case-by-case basis.

With that confidence, Congress might show a greater willingness to substantially reduce the degree of 

prescriptivism in acquisition law. This shift would allow Executive Branch personnel greater leeway to 

make decisions based on what makes sense at the moment, rather than basing decisions on 

preestablished policies written without transaction-specific context.

To promote confidence among congressional stakeholders, DoD would also need to empower workinglevel people, trust them to do good work, and provide for real consequences if they failed to do good 

work. Improved data analytics capabilities, in both DoD and Congress, would be a necessary but 

insufficient condition for this type of a grand bargain to be realized.

If DoD intends to improve its data analytics capabilities, it should (a) comply with congressional opendata requirements, (b) ensure defense agencies are aware of the data that is available, and (c) improve 

the quality of its data analytics workforce. The CMO is the logical entity to direct these changes, but 

ultimately, they will have to be carried out at the lower levels.

Implementation

Legislative Branch

§ There are no statutory changes required for this recommendation.

Executive Branch

§ Issue a memorandum implementing Sections 911–913 of the FY 2018 NDAA open-data 

mandate.

§ Use existing hiring and scholarship authorities to bolster DoD agencies’ data analytics 

workforce (see Appendix C).

Implications for Other Agencies

§ If DoD’s efforts to improve strategic data analytics are successful, they may serve as a template 

for similar future efforts by other agencies.